Particle swarm optimization (PSO) is a well-known optimization algorithm that shows good performances in solving different optimization problems. However, the PSO usually suffers from slow convergence. In this article, a reinforcement-learning-based parameter adaptation method (RLAM) is developed to enhance the PSO convergence by designing a network to control the coefficients of the PSO. Moreover, based on the RLAM, a new reinforcement-learning-based PSO (RLPSO) algorithm is designed. To investigate the performance of the RLAM and RLPSO, experiments on 28 CEC 2013 benchmark functions were carried out to compare with other adaptation methods and PSO variants. The reported computational results showed that the proposed RLAM is efficient and effective and that the proposed RLPSO is superior to several state-of-the-art PSO variants.

Currently, integrated humidity sensors with fast-response time are widely needed. The most commonly used polyimide capacitive humidity sensor has a long response time, which is difficult to meet the need for a fast response. Most studies focusing on technology and materials have a high cost and are difficult to ensure compatability with the CMOS process. The dynamic compensation method can shorten the response time by only adding digital circuits or software processing. However, conventional compensation technology is not suitable for humidity sensors due to temperature coupling. This paper proposes a new dynamic compensation method for humidity sensors based on the decoupling of temperature factors by analyzing the coupling relationship between sensor dynamic characteristics and temperature. Simulations and experiments were used to verify the proposed method. The experimental results show that the proposed method reduces the humidity response time of the sensor by 85.6%. The proposed method can effectively shorten the response time of humidity sensors.

A p-Si/n⁺-6H-SiC heterojunction non-ultraviolet photodiode is fabricated on 6H-SiC substrate. Non-UV light operation of the SiC devices is initially realised. J-V measurement indicates that the heterojunction has good rectifying behaviour with a turn-on voltage of ∼1 V. Under non-UV light illumination of 0.6 W/cm², the device exhibits good photo-response with a maximum open circuit voltage V_oc of 92.0 mV and photocurrent I_ph of 0.6 mA/cm².

A Temperature sensor plays an important role in modern high-performance processing chips. A temperature sensing core with a high sensing accuracy is proposed in this article to meet the increasing demand for on-chip thermal monitoring. A comprehensive analysis of the thermal characteristics in subthreshold current is presented to achieve optimal sensing accuracy. The thermal information contained in subthreshold current is converted into a frequency which can be easily digitized in processing chips. The dynamic offset compensation (DOC) method is proposed to improve the supply sensitivity. In addition, small area and low power are also achieved due to its simple structure and low working current. The sensor is designed and implemented in the standard 0.153-μm CMOS technology. After a two-point calibration and systematic error removal (SER), the proposed sensor has a measuring inaccuracy of +0.57/−0.65 °C (3σ) within the temperature range of −40 °C ~ 120°C;. The occupied area and power consumption of the sensor are only 860 μm2 and 1.3 μW, respectively. With the help of DOC, the sensor can achieve a supply sensitivity of 2.1 °C/V. This combination of high accuracy, small area, and low energy consumption makes this sensor suitable for on-chip dense thermal monitoring applications.

Modern high-performance processing chips utilize on-chip temperature sensors to monitor the temperature. In addition to a small area and low power, there is a strong demand for high accuracy with low-cost calibration. This brief presents an accurate sensor that converts the thermal information contained in the current ratio of two subthreshold MOSFETs to duty cycle. The proportional operation ensures that the slope of the linear sensing relationship remains unaffected by process variation, thereby guaranteeing the achievement of one-point trimming. The dynamic slope compensation (DSC) method is proposed to improve sensor supply sensitivity. Implemented in a 0.153-μm CMOS process, the proposed sensor occupies only 1632 μm2 and consumes 0.7 μW at room temperature. The measured inaccuracy is +1.2/–1.0 ∘C (3σC) in –20∘C~120∘C after one-point calibration and systematic error removal. With the assistance of DSC, the sensor can achieve a supply sensitivity of 4.3 ∘C/V.

The stability and accuracy of explicit high-order finite difference (HOFD) algorithms have been research hotspots in different fields. To improve the stability and accuracy of the HOFD algorithms in thermal simulations, we present a Lax-Wendroff high-order finite difference (LHOFD) algorithm to solve the 2D transient heat transfer equation in this paper and develop an improved LHOFD (IHOFD) algorithm to improve the stability of the LHOFD algorithm. The formulas of the general high-order central FD (HOCFD) coefficients and the truncation error coefficient as well as the high-order non-central FD (HONFD) coefficients and the truncation error coefficient of the fourth-order spatial derivative are derived concisely in a different way. Furthermore, a unified analytical formula of the general HOCFD and HONFD coefficients, which can calculate the spatial derivative of any integer order, is derived. A new strategy of combination with the HOCFD and HONFD approximations under the same high-order accuracy as the internal computational domain is proposed to calculate the mixed derivatives of the boundary domains with high accuracy, no additional computational cost, and easy implementation. Then, the accuracy analysis, stability analysis, and comparative analysis of numerical simulation results obtained by the LHOFD and IHOFD algorithms with the exact solution show the correctness and validity of the proposed algorithms and their stability formulas, and the advantages of the proposed algorithms. The proposed algorithms are valid under both symmetric and asymmetric boundary conditions. The stability factor of the LHOFD algorithm is slightly higher than that of the conventional algorithm. The stability factor of the IHOFD algorithm is twice that of the conventional algorithm, and the maximum absolute error of the thermal simulation is within 0.015 (°C).